Iris

Importpandasaspd
Fromsklearn.datasetsimportload_iris
Fromsklearn.linear_modelimportLogisticRegression
Fromsklearn.model_selectionimporttrain_test_split
Fromsklearn.metricsimportaccuracy_score
#loadtheirisdataset
Iris=load_iris()
#createadataframefromthedataset
Df=pd.DataFrame(iris.data,columns=iris.feature_names)
Df[‘target’]=iris.target
#viewbasicstatisticaldetailsofthedifferentspecies
Print(“StatisticaldetailsofIris-setosa:”)
Print(df[df[‘target’]==0].describe())
Print(“StatisticaldetailsofIris-versicolor:”)
Print(df[df[‘target’]==1].describe())
Print(“StatisticaldetailsofIris-virginica:”)
Print(df[df[‘target’]==2].describe())
#splitthedataintotrainingandtestingsets
X=df.iloc[:,:-1]
Y=df.iloc[:,-1]
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
#fitalogisticregressionmodel
Logreg=LogisticRegression()
Logreg.fit(X_train,y_train)
#makepredictionsonthetestset
Y_pred=logreg.predict(X_test)
#calculatetheaccuracyofthemodel
Accuracy=accuracy_score(y_test,y_pred)
Print(“Accuracyofthelogisticregressionmodel:”,accuracy